




"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )









"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				"""▁"""

lowerCAmelCase				:     str					=				{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase				:     Union[str, Any]					=				{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase				:     Any					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase				:     List[Any]					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}



def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   collections.OrderedDict()
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as reader:
						lowerCamelCase      =   reader.readlines()
			for index, token in enumerate(snake_case__							):
						lowerCamelCase      =   token.rstrip("""\n"""							)
						lowerCamelCase      =   index
			return vocab






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[UNK]"						,					_a="[PAD]"						,					_a="[CLS]"						,					_a="[MASK]"						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_a						,					eos_token=_a						,					sep_token=_a						,					unk_token=_a						,					pad_token=_a						,					cls_token=_a						,					mask_token=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)

							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							lowerCamelCase      =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCamelCase      =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10					):
										lowerCamelCase      =   f'[unused{i}]'
										lowerCamelCase      =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCamelCase      =   12
							lowerCamelCase      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_a					)





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d
							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_a						,					token_ids_a=_a						,					already_has_special_tokens=_a					)

							if token_ids_a is None:
										return ([0] * len(_a					)) + [1]
							return ([0] * len(_a					)) + [1] + ([0] * len(_a					)) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							lowerCamelCase      =   [self.sep_token_id]

							if token_ids_a is None:
										return len(token_ids_a + sep					) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.fairseq_offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase      =   self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   """""".join(_a					).replace(_a						,					""" """					).strip()
							return out_string





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""

							if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
							lowerCamelCase      =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep



